Search Results for author:
Found 7 papers, 5 papers with code
GTC: GNN-Transformer Co-contrastive Learning for Self-supervised Heterogeneous Graph Representation
So, can we propose a novel framework to combine GNN and Transformer, integrating both GNN's local information aggregation and Transformer's global information modeling ability to eliminate the over-smoothing problem?
SpikeGraphormer: A High-Performance Graph Transformer with Spiking Graph Attention
In this work, we propose a novel insight into integrating SNNs with Graph Transformers and design a Spiking Graph Attention (SGA) module.
Can We Transfer Noise Patterns? A Multi-environment Spectrum Analysis Model Using Generated Cases
To make the analysis model applicable to more environments, we propose a noise patterns transferring model, which takes the spectrum of standard water samples in different environments as cases and learns the differences in their noise patterns, thus enabling noise patterns to transfer to unknown samples.
Spectrum-BERT: Pre-training of Deep Bidirectional Transformers for Spectral Classification of Chinese Liquors
Spectral detection technology, as a non-invasive method for rapid detection of substances, combined with deep learning algorithms, has been widely used in food detection.
SCAI: A Spectral data Classification framework with Adaptive Inference for the IoT platform
To address the above issues, we propose a Spectral data Classification framework with Adaptive Inference.
Motifs-based Recommender System via Hypergraph Convolution and Contrastive Learning
Based on this, we also propose a hierarchical self-supervised learning model, which realizes two levels of self-supervised learning within and between channels, which improves the ability of self-supervised tasks to autonomously mine different levels of potential information.
MHNF: Multi-hop Heterogeneous Neighborhood information Fusion graph representation learning
Finally, a hierarchical semantic attention fusion model (HSAF) is constructed, which can efficiently integrate different-hop and different-path neighborhood information.
